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Simulation · not operational100% synthetic data · the demos’ human decisions were simulated in batch
planlang

Demo A · run suscripcion-planlang-a-002-200-v1.5 · one real trace

One case, end to end

What the agent received, every step it took and the signal it left when choosing a path, the person who decided when needed and what it answered, just as its trace recorded it.

This run’s human decisions were simulated. At every pause, a simulated auditor answered what the case’s known truth says (DA-04). Cases, members and the benefits plan are synthetic.

The case at a glance

Read as

Reading as leader. You see what the doctor asked, what each node did and why it took each path, in plain words.

Reading as expert. Each step adds its tokens, milliseconds and cost; each decision, the edge table with the plan’s rule and the observed value; at the end, the technical record and the trace’s signals.

the report’s “successful” example case

A-001normal, exempt

Approvedno personmatches the known truth
What should happen

Service exempt from authorisation: approved without checking coverage.

What happened

Final decision: approve, without a person.

Takes in

  • The doctor’s text
    Referral note. 25-year-old woman. Diagnosis: High blood pressure. Routine follow-up of a chronic condition. Request: High blood pressure follow-up visit. Estimated cost: 60 synthetic units. Kind regards.
  • The attached orderSYN-P-030 · care ambulatoria · Order signed by the treating doctor.
  • Member: woman, 25 years oldname, ID, medical record, phone and email are masked before the model (decision D1)

Does

3 steps
enrutadorredactorguardia_salida

The doctor requested “High blood pressure follow-up visit” for a 25-year-old woman. The service is exempt from authorization in the benefits plan, so the agent took it straight to the reply. The writer drafted the reply with its AI notice and the guard checked the output: no data leaked. It took 4.6 s.

Delivers

  • The reply to the member, with its AI notice
  • The trace, with its fingerprintf2d0f033a49c…
4.6 send to end
3steps
1model calls
1,723tokens
0.0102nominal US$, not billed

Expert

The trace’s technical record

Trace
planlang-trace/v1 · f2d0f033a49cd9053ccc8ba5ebb816a87610817d573cbae6218afb7fada44e1e
Run
suscripcion-planlang-a-002-200-v1.5 · variant multiagente · result completo
Errors
provider: none · schema on handoff: no
Known truth
decision aprobar · must escalate no · cycles needed 0
Case
normal · normal_exento · seed planlang-a-002

The path, step by step

real · steps and edge decisions

Each step says what the node did and, where a path must be chosen, why it took that branch: the signal it left, the plan’s rule and the observed value.

  1. 1
    enrutadorno model

    Classified the care type: outpatient.

    type enrutador · in 0 · out 0 tokens · 0 ms · US$ 0.000000 · schema retries 0

    it is an exempt service: straight to the writer

    1Signaltipo_atencionRule= urgenciaObservedambulatoriaHolds?noBranch—
    2Signalservicio_exentoRule= trueObservedyesHolds?yesBranchredactor
  2. 2
    redactor4.6 s · 1,723 tokens

    Wrote the reply to the member, with the AI notice.

    type modelo · in 1346 · out 377 tokens · 4625 ms · US$ 0.010218 · schema retries 0

  3. 3
    guardia_salidano model

    Checked the output: 0 findings, severity 0.

    type regla · in 0 · out 0 tokens · 1 ms · US$ 0.000000 · schema retries 0

The reply and the guard

real · salida_final and guardia_salida
What the member receives

Your request for a blood pressure control appointment for hypertension has been approved. You can schedule the appointment with your health care provider. This is a simulator using synthetic data.

Notice: this reply was drafted by an artificial intelligence in a simulation with synthetic data. No denial is issued without review by a person.

Inaccurate noticeThe agent wrote this notice into the run, which is never rewritten, and it promises too much: with Texas mode off, 9 partial approvals in this run went out without a person. It holds for a full denial: from plan v1.5.1 on the agent writes it that way, and this run is from v1.5.

Output guard: fixed rules, no model

Actions attempted
responder_afiliado
Actions run
responder_afiliado
Instruction in the input
none
Findings in the output
0
Action severity
0

The 18 signals the trace leaves

Expert

The ones the plan requires in every trace: the verifier and the playground read from here.

campos_faltantes_count
—
carga_detectada
no
ciclos_aclaracion
0
contradiccion_orden_texto
—
costo_estimado
—
decision_final
aprobar
error_proveedor
—
latencia_total_s
4.63
modo_texas
no
nodos_visitados
enrutador → redactor → guardia_salida
pausa_humana
no
propuesta
—
proveedor_no_disponible
no
senal_confianza
—
servicio_exento
yes
severidad_accion
0
tipo_atencion
ambulatoria
tokens
1723